{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T10:34:52Z","timestamp":1774434892093,"version":"3.50.1"},"reference-count":72,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2023,11,6]],"date-time":"2023-11-06T00:00:00Z","timestamp":1699228800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Research and Development Project of Hubei Province","award":["2021BCA219"],"award-info":[{"award-number":["2021BCA219"]}]},{"name":"Key Research and Development Project of Hubei Province","award":["2021BID009"],"award-info":[{"award-number":["2021BID009"]}]},{"name":"Key Research and Development Project of Hubei Province","award":["ZRZY2022KJ17"],"award-info":[{"award-number":["ZRZY2022KJ17"]}]},{"name":"key research and development program of Hubei province","award":["2021BCA219"],"award-info":[{"award-number":["2021BCA219"]}]},{"name":"key research and development program of Hubei province","award":["2021BID009"],"award-info":[{"award-number":["2021BID009"]}]},{"name":"key research and development program of Hubei province","award":["ZRZY2022KJ17"],"award-info":[{"award-number":["ZRZY2022KJ17"]}]},{"name":"Hubei Provincial Department of Natural Resources","award":["2021BCA219"],"award-info":[{"award-number":["2021BCA219"]}]},{"name":"Hubei Provincial Department of Natural Resources","award":["2021BID009"],"award-info":[{"award-number":["2021BID009"]}]},{"name":"Hubei Provincial Department of Natural Resources","award":["ZRZY2022KJ17"],"award-info":[{"award-number":["ZRZY2022KJ17"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Landslide susceptibility mapping (LSM) is significant for landslide risk assessment. However, there remains no consensus on which method is optimal for LSM. This study implements a dynamic approach to landslide hazard mapping by integrating spatio-temporal probability analysis with time-varying ground deformation velocity derived from the MT-InSAR (Multi-Temporal InSAR) method. Reliable landslide susceptibility maps (LSMs) can inform landslide risk managers and government officials. First, sixteen factors were selected to construct a causal factor system for LSM. Next, Pearson correlation analysis, multicollinearity analysis, information gain ratio, and GeoDetector methods were applied to remove the least important factors of STI, plan curvature, TRI, and slope length. Subsequently, information quantity (IQ), logistic regression (LR), frequency ratio (FR), artificial neural network (ANN), random forest (RF), support vector machine (SVM), and convolutional neural network (CNN) methods were performed to construct the LSM. The results showed that the distance to a river, slope angle, distance from structure, and engineering geological rock group were the main factors controlling landslide development. A comprehensive set of statistical indicators was employed to evaluate these methods\u2019 effectiveness; sensitivity, F1-measure, and AUC (area under the curve) were calculated and subsequently compared to assess the performance of the methods. Machine learning methods\u2019 training and prediction accuracy were higher than those of statistical methods. The AUC values of the IQ, FR, LR, BP-ANN, RBF-ANN, RF, SVM, and CNN methods were 0.810, 0.854, 0.828, 0.895, 0.916, 0.932, 0.948, and 0.957, respectively. Although the performance order varied for other statistical indicators, overall, the CNN method was the best, while the BP-ANN and RBF-ANN method was the worst among the five examined machine methods. Hence, adopting the CNN approach in this study can enhance LSM accuracy, catering to the needs of planners and government agencies responsible for managing landslide-prone areas and preventing landslide-induced disasters.<\/jats:p>","DOI":"10.3390\/rs15215256","type":"journal-article","created":{"date-parts":[[2023,11,6]],"date-time":"2023-11-06T13:24:53Z","timestamp":1699277093000},"page":"5256","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Data-Driven Landslide Spatial Prediction and Deformation Monitoring: A Case Study of Shiyan City, China"],"prefix":"10.3390","volume":"15","author":[{"given":"Yifan","family":"Sheng","sequence":"first","affiliation":[{"name":"Institute of Geological Survey, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Guangli","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute of Geological Survey, China University of Geosciences, Wuhan 430074, China"},{"name":"Faculty of Engineering, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Bijing","family":"Jin","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, China University of Geosciences, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4702-4021","authenticated-orcid":false,"given":"Chao","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Yuanyao","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Geological Survey, China University of Geosciences, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6272-1618","authenticated-orcid":false,"given":"Weitao","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"124602","DOI":"10.1016\/j.jhydrol.2020.124602","article-title":"Evaluating the usage of tree-based ensemble methods in groundwater spring potential mapping","volume":"583","author":"Chen","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_2","first-page":"102093","article-title":"Satellite-derived rainfall thresholds for landslide early warning in Bogowonto Catchment, Central Java, Indonesia","volume":"89","author":"Chikalamo","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"104580","DOI":"10.1016\/j.catena.2020.104580","article-title":"Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping","volume":"191","author":"Huang","year":"2020","journal-title":"Catena"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.jhydrol.2019.03.073","article-title":"A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods","volume":"573","author":"Khosravi","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"134413","DOI":"10.1016\/j.scitotenv.2019.134413","article-title":"A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area","volume":"701","author":"Hoang","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"101104","DOI":"10.1016\/j.gsf.2020.10.009","article-title":"Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression (SVR) with GWO, BAT and COA algorithms","volume":"12","author":"Balogun","year":"2021","journal-title":"Geosci. Front."},{"key":"ref_7","first-page":"5235","article-title":"Landslide Detection Using Densely Connected Convolutional Networks and Environmental Conditions. IEEE J. Sel. Top. Appl. Earth Obs","volume":"14","author":"Cai","year":"2021","journal-title":"Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1016\/j.ijdrr.2018.01.011","article-title":"Risk modelling as a tool to support natural hazard risk management in New Zealand local government","volume":"28","author":"Crawford","year":"2018","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1007\/s10064-022-02890-x","article-title":"Threshold assessment of rainfall-induced landslides in Sangzhi County: Statistical analysis and physical model","volume":"81","author":"Sheng","year":"2022","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1016\/j.gsf.2020.05.010","article-title":"Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia","volume":"12","author":"Youssef","year":"2021","journal-title":"Geosci. Front."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Chang, Z., Du, Z., Zhang, F., Huang, F., Chen, J., Li, W., and Guo, Z. (2020). Landslide Susceptibility Prediction Based on Remote Sensing Images and GIS: Comparisons of Supervised and Unsupervised Machine Learning Models. Remote Sens., 12.","DOI":"10.3390\/rs12030502"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4287","DOI":"10.1007\/s00477-022-02263-6","article-title":"Landslide susceptibility mapping using deep learning models in Ardabil province, Iran","volume":"36","author":"Hamedi","year":"2022","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.geomorph.2018.06.006","article-title":"Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia","volume":"318","author":"Aditian","year":"2018","journal-title":"Geomorphology"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.enggeo.2014.09.001","article-title":"GIS-based prediction method of landslide susceptibility using a rainfall infiltration-groundwater flow model","volume":"182","author":"Kim","year":"2014","journal-title":"Eng. Geol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"101311","DOI":"10.1016\/j.gsf.2021.101311","article-title":"Integrating deep learning and logging data analytics for lithofacies classification and 3D modeling of tight sandstone reservoirs","volume":"13","author":"Liu","year":"2022","journal-title":"Geosci. Front."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2920","DOI":"10.1029\/2019JF005204","article-title":"A Multi-Phase Mass Flow Model","volume":"124","author":"Pudasaini","year":"2019","journal-title":"J. Geophys. Res. Earth Surf."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"104459","DOI":"10.1016\/j.earscirev.2023.104459","article-title":"A critical review about generic subaerial landslide-tsunami experiments and options for a needed step change","volume":"242","author":"Heller","year":"2023","journal-title":"Earth-Sci. Rev."},{"key":"ref_18","first-page":"02913","article-title":"Physical model test on deformation and failure mechanism of deposit landslide under gradient rainfall","volume":"81","author":"Wang","year":"2022","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.enggeo.2018.08.016","article-title":"Centrifuge model test on the retrogressive landslide subjected to reservoir water level fluctuation","volume":"245","author":"Miao","year":"2018","journal-title":"Eng. Geol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"101378","DOI":"10.1016\/j.gsf.2022.101378","article-title":"Centrifugal model test on a riverine landslide in the Three Gorges Reservoir induced by rainfall and water level fluctuation","volume":"13","author":"Miao","year":"2022","journal-title":"Geosci. Front."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1007\/s00445-016-1069-5","article-title":"The influence of slope-angle ratio on the dynamics of granular flows: Insights from laboratory experiments","volume":"78","author":"Sulpizio","year":"2016","journal-title":"Bull. Volcanol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1139\/cgj-2016-0104","article-title":"2014 Canadian Geotechnical Colloquium: Landslide runout analysis\u2014Current practice and challenges","volume":"54","author":"McDougall","year":"2017","journal-title":"Can. Geotech. J."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.enggeo.2012.08.006","article-title":"Discrete element modeling of a rainfall-induced flowslide","volume":"149\u2013150","author":"Li","year":"2012","journal-title":"Eng. Geol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"106138","DOI":"10.1016\/j.catena.2022.106138","article-title":"Formation and evolution of a giant old deposit in the First Bend of the Yangtze River on the southeastern margin of the Qinghai-Tibet Plateau","volume":"213","author":"Li","year":"2022","journal-title":"Catena"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"105118","DOI":"10.1016\/j.compgeo.2022.105118","article-title":"A multi-field and fluid\u2013solid coupling method for porous media based on DEM-PNM","volume":"154","author":"Zhu","year":"2023","journal-title":"Comput. Geotech."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"134979","DOI":"10.1016\/j.scitotenv.2019.134979","article-title":"Modeling flood susceptibility using data-driven approaches of naive Bayes tree, alternating decision tree, and random forest methods","volume":"701","author":"Chen","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"125615","DOI":"10.1016\/j.jhydrol.2020.125615","article-title":"Can deep learning algorithms outperform benchmark machine learning algorithms in flood susceptibility modeling?","volume":"592","author":"Pham","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.earscirev.2018.03.001","article-title":"A review of statistically-based landslide susceptibility models","volume":"180","author":"Reichenbach","year":"2018","journal-title":"Earth-Sci. Rev."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1016\/j.gsf.2020.09.004","article-title":"GIS-based landslide susceptibility modeling: A comparison between fuzzy multi-criteria and machine learning algorithms","volume":"12","author":"Ali","year":"2021","journal-title":"Geosci. Front."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1007\/s10346-017-0919-3","article-title":"Comparison of statistical methods and multi-time validation for the determination of the shallow landslide rainfall thresholds","volume":"15","author":"Galanti","year":"2018","journal-title":"Landslides"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1203","DOI":"10.1016\/j.gsf.2019.10.008","article-title":"Is multi-hazard mapping effective in assessing natural hazards and integrated watershed management?","volume":"11","author":"Pourghasemi","year":"2020","journal-title":"Geosci. Front."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"139937","DOI":"10.1016\/j.scitotenv.2020.139937","article-title":"Spatial prediction of landslide susceptibility using hybrid support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS) with various metaheuristic algorithms","volume":"741","author":"Panahi","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1007\/s12517-012-0807-z","article-title":"Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya","volume":"7","author":"Regmi","year":"2013","journal-title":"Arab. J. Geosci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.geomorph.2017.10.018","article-title":"Optimizing landslide susceptibility zonation: Effects of DEM spatial resolution and slope unit delineation on logistic regression models","volume":"301","author":"Marchesini","year":"2018","journal-title":"Geomorphology"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1007\/s12665-017-6839-7","article-title":"Mapping landslide susceptibility with frequency ratio, statistical index, and weights of evidence models: A case study in northern Iran","volume":"76","author":"Razavizadeh","year":"2017","journal-title":"Environ. Earth Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"104188","DOI":"10.1016\/j.catena.2019.104188","article-title":"A similarity-based approach to sampling absence data for landslide susceptibility mapping using data-driven methods","volume":"183","author":"Zhu","year":"2019","journal-title":"Catena"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.cageo.2011.04.012","article-title":"An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm","volume":"38","author":"Akgun","year":"2012","journal-title":"Comput. Geosci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1007\/s10064-019-01548-5","article-title":"On the use of hierarchical fuzzy inference systems (HFIS) in expert-based landslide susceptibility mapping: The central part of the Rif Mountains (Morocco)","volume":"79","author":"Ozer","year":"2019","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"101203","DOI":"10.1016\/j.gsf.2021.101203","article-title":"Applying deep learning and benchmark machine learning algorithms for landslide susceptibility modelling in Rorachu river basin of Sikkim Himalaya, India","volume":"12","author":"Mandal","year":"2021","journal-title":"Geosci. Front."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jenvman.2018.03.089","article-title":"Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping","volume":"217","author":"Valavi","year":"2018","journal-title":"J. Environ. Manag."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1133","DOI":"10.1016\/j.scitotenv.2017.10.037","article-title":"Mapping flood susceptibility in mountainous areas on a national scale in China","volume":"615","author":"Zhao","year":"2018","journal-title":"Sci. Total. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1007\/s00477-021-02032-x","article-title":"Stacking ensemble of deep learning methods for landslide susceptibility mapping in the Three Gorges Reservoir area, China","volume":"36","author":"Li","year":"2021","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"104240","DOI":"10.1016\/j.catena.2019.104240","article-title":"Artificial neural network ensembles applied to the mapping of landslide susceptibility","volume":"184","author":"Bragagnolo","year":"2020","journal-title":"Catena"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1007\/s10064-017-1034-3","article-title":"Landslide susceptibility mapping at Ovac\u0131k-Karab\u00fck (Turkey) using different artificial neural network models: Comparison of training algorithms","volume":"78","author":"Can","year":"2017","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"104358","DOI":"10.1016\/j.catena.2019.104358","article-title":"Systematic sample subdividing strategy for training landslide susceptibility models","volume":"187","author":"Sameen","year":"2020","journal-title":"Catena"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1080\/19475705.2018.1481147","article-title":"Evaluation of landslide susceptibility mapping by evidential belief function, logistic regression and support vector machine models. Geomat","volume":"9","author":"Oh","year":"2018","journal-title":"Nat. Hazards Risk"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"3597","DOI":"10.1007\/s00477-022-02212-3","article-title":"Prediction of spatial landslide susceptibility applying the novel ensembles of CNN, GLM and random forest in the Indian Himalayan region","volume":"36","author":"Saha","year":"2022","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"107623","DOI":"10.1016\/j.geomorph.2021.107623","article-title":"A hybrid optimization method of factor screening predicated on GeoDetector and Random Forest for Landslide Susceptibility Mapping","volume":"379","author":"Sun","year":"2021","journal-title":"Geomorphology"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/s10346-019-01274-9","article-title":"A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction","volume":"17","author":"Huang","year":"2019","journal-title":"Landslides"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"104445","DOI":"10.1016\/j.cageo.2020.104445","article-title":"Comparative study of landslide susceptibility mapping with different recurrent neural networks","volume":"138","author":"Wang","year":"2020","journal-title":"Comput. Geosci."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"104249","DOI":"10.1016\/j.catena.2019.104249","article-title":"Application of convolutional neural networks featuring Bayesian optimization for landslide susceptibility assessment","volume":"186","author":"Sameen","year":"2020","journal-title":"Catena"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1016\/j.gsf.2019.10.001","article-title":"How do machine learning techniques help in increasing accuracy of landslide susceptibility maps?","volume":"11","author":"Achour","year":"2020","journal-title":"Geosci. Front."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1007\/s11069-018-3449-y","article-title":"A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping","volume":"94","author":"Ghorbanzadeh","year":"2018","journal-title":"Nat. Hazards"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"101425","DOI":"10.1016\/j.gsf.2022.101425","article-title":"Multi-hazard susceptibility mapping based on Convolutional Neural Networks","volume":"13","author":"Ullah","year":"2022","journal-title":"Geosci. Front."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"3460","DOI":"10.1109\/TGRS.2011.2124465","article-title":"A New Algorithm for Processing Interferometric Data-Stacks: SqueeSAR","volume":"49","author":"Ferretti","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2375","DOI":"10.1109\/TGRS.2002.803792","article-title":"A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms","volume":"40","author":"Berardino","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"111983","DOI":"10.1016\/j.rse.2020.111983","article-title":"InSAR-based detection method for mapping and monitoring slow-moving landslides in remote regions with steep and mountainous terrain: An application to Nepal","volume":"249","author":"Bekaert","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"e2020JF005898","DOI":"10.1029\/2020JF005898","article-title":"Inferring the Subsurface Geometry and Strength of Slow-Moving Landslides Using 3-D Velocity Measurements From the NASA\/JPL UAVSAR","volume":"126","author":"Handwerger","year":"2021","journal-title":"J. Geophys. Res. Earth Surf."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"113669","DOI":"10.1016\/j.rse.2023.113669","article-title":"The 21 July 2020 Shaziba landslide in China: Results from multi-source satellite remote sensing","volume":"295","author":"Wang","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1007\/s10346-019-01276-7","article-title":"Long-term InSAR, borehole inclinometer, and rainfall records provide insight into the mechanism and activity patterns of an extremely slow urbanized landslide","volume":"17","author":"Wasowski","year":"2019","journal-title":"Landslides"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1585","DOI":"10.1007\/s10346-021-01796-1","article-title":"Enhanced dynamic landslide hazard mapping using MT-InSAR method in the Three Gorges Reservoir Area","volume":"19","author":"Zhou","year":"2022","journal-title":"Landslides"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"106590","DOI":"10.1016\/j.enggeo.2022.106590","article-title":"Characteristic comparison of seepage-driven and buoyancy-driven landslides in Three Gorges Reservoir area, China","volume":"301","author":"Zhou","year":"2022","journal-title":"Eng. Geol."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"2499","DOI":"10.1007\/s10346-021-01662-0","article-title":"Spatiotemporal modelling of rainfall-induced landslides using machine learning","volume":"18","author":"Ng","year":"2021","journal-title":"Landslides"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"8647","DOI":"10.1007\/s12665-015-4028-0","article-title":"Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio, logistic regression, and fuzzy logic methods at the central Zab basin, Iran","volume":"73","author":"Shahabi","year":"2015","journal-title":"Environ. Earth Sci."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1109\/MGRS.2019.2954395","article-title":"Entering the Era of Earth Observation-Based Landslide Warning Systems: A Novel and Exciting Framework","volume":"8","author":"Dai","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"106033","DOI":"10.1016\/j.enggeo.2021.106033","article-title":"Integration of Sentinel-1 and ALOS\/PALSAR-2 SAR datasets for mapping active landslides along the Jinsha River corridor, China","volume":"284","author":"Liu","year":"2021","journal-title":"Eng. Geol."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"112057","DOI":"10.1016\/j.rse.2020.112057","article-title":"Internal kinematics of the Slumgullion landslide (USA) from high-resolution UAVSAR InSAR data","volume":"251","author":"Hu","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"5248","DOI":"10.1109\/JSTARS.2019.2953339","article-title":"Satellite Interferometry as a Tool for Early Warning and Aiding Decision Making in an Open-Pit Mine","volume":"12","author":"Intrieri","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"2865","DOI":"10.1007\/s10064-018-1281-y","article-title":"A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling","volume":"78","author":"Pham","year":"2018","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"137320","DOI":"10.1016\/j.scitotenv.2020.137320","article-title":"Different sampling strategies for predicting landslide susceptibilities are deemed less consequential with deep learning","volume":"720","author":"Dou","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1408","DOI":"10.1080\/17538947.2020.1718785","article-title":"A tree-based intelligence ensemble approach for spatial prediction of potential groundwater","volume":"13","author":"Avand","year":"2020","journal-title":"Int. J. Digit. Earth"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"104899","DOI":"10.1016\/j.cageo.2021.104899","article-title":"Landslide susceptibility assessment using weights-of-evidence model and cluster analysis along the highways in the Hubei section of the Three Gorges Reservoir Area","volume":"156","author":"Chen","year":"2021","journal-title":"Comput. Geosci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/21\/5256\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:18:17Z","timestamp":1760131097000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/21\/5256"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,6]]},"references-count":72,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2023,11]]}},"alternative-id":["rs15215256"],"URL":"https:\/\/doi.org\/10.3390\/rs15215256","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,6]]}}}